Higher tree diversity is linked to higher tree mortality
Bibliographic record
Abstract
Examining the relationship between tree diversity and ecosystem functioning has been a recent focus of forest ecology. Particular emphasis has been given to the impact of tree diversity on productivity and to its potential to mitigate negative global change effects; however, little attention has been paid to tree mortality. This is critical because both tree mortality and productivity underpin forest ecosystem dynamics and therefore forest carbon sequestration. Neglecting tree mortality leaves a large part of the picture undocumented. Here we show that increasingly diverse forest stands have increasingly high mortality probabilities. We found that the most species-rich stands in temperate biomes had mortality probabilities more than sevenfold higher than monospecific stands (∼0.6% year−1 in monospecific stands to 4.0% year−1 in the most species-rich stands) while in boreal stands increases were less pronounced but still significant (∼1.1% year−1 in monospecific stands to 1.8% year−1 in the most species-rich stands). Tree species richness was the third-most-important predictor of mortality in our models in temperate forests and the fifth-most-important predictor in boreal forests. Our results highlight that while the promotion of tree diversity undoubtedly has many positive effects on ecosystem functioning and the services that trees provide to humanity, it remains important to consider all aspects of forest dynamics in order to properly predict the implications of maintaining and promoting tree diversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".